scholarly journals Developing random satisfiability logic programming in Hopfield neural network

2020 ◽  
Author(s):  
Hamza Abubakar ◽  
Saratha Sathasivam
Author(s):  
Vigneshwer Kathirvel ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam

Imperialist Competitive algorithm (ICA) is a robust training algorithm inspired by the socio-politically motivated strategy. This paper focuses on utilizing a hybridized ICA with Hopfield Neural Network on a 3-Satisfiability (3-SAT) logic programming. Eventually the performance of the proposed algorithm will be compared to other 2 algorithms, which are HNN-3SATES (ES) and HNN-3SATGA (GA). The performance shall be evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squares Error (SSE), Schwarz Bayesian Criterion (SBC), Global Minima Ratio and Computation Time (CPU time). The expected outcome will portray that the IC algorithm will outperform the other two algorithms in doing 3-SAT logic programming.


Author(s):  
Shehab Abdulhabib Saeed Alzaeemi ◽  
◽  
Saratha Sathasivam ◽  
Muraly Velavan

2019 ◽  
Author(s):  
Shehab Abdulhabib Alzaeemi ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam

2019 ◽  
Vol 1366 ◽  
pp. 012094
Author(s):  
M S M Kasihmuddin ◽  
M A Mansor ◽  
S Alzaeemi ◽  
M F M Basir ◽  
S Sathasivam

2018 ◽  
Vol 47 (6) ◽  
pp. 1327-1335 ◽  
Author(s):  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Mohd Asyraf Mansor ◽  
Saratha Sathasivam

Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.


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